Dynamic snow surface aerodynamic roughness lengths (z0) characterized by snow depths using LIDAR

A shallow, seasonal snowpack is rarely homogeneous in depth, layer characteristics, or surface structure throughout an entire winter. Aerodynamic roughness length (z0) is typically considered a static parameter within hydrologic and atmospheric models. However, observations have shown that z0 is a dynamic variable, necessitating accurate spatial and temporal measurements of z0. Terrestrial LiDAR data were collected at nine different study sites in northwest Colorado from the 2019-2020 winter season to observe variability of the snowpack surface. Geometric z0 and snow depth (ds) observation over 112 site visits illustrated a change in z0 as a function of ds. Values of z0 decrease during initial snow accumulation, as the snow conforms to the underlying terrain. Once the snowpack is sufficiently deep, which depends on the height of the ground surface roughness features, the surface becomes more uniform. As melt begins, z0 increases, when the snow surface becomes more irregular. The correla..., , # Dynamic snow surface aerodynamic roughness lengths (z0) characterized by snow depths using LIDAR

Dataset DOI: [10.5061/dryad.80gb5mm35](10.5061/dryad.80gb5mm35)

## Description of the data and file structure

Data are point clouds of the Area of Interest (AOI) throughout the 2019-2020 winter season of 9 field sites. The accompanied excel sheet describes the site visit information and the associated label to each point cloud.

## Code/software

Point clouds were built and modified in Cloud Compare ([CloudCompare - Open Source project) . ](https://www.cloudcompare.org/)However, any software that allows the use of point clouds should work with the data. Data are in .dat, but will need to be converted to .txt to run with Cloud Compare. The .dat files are used in the interpolation software (https://www.goldensoftware.com/products/surfer/), which were then converted to the attached .dat files for use in the MATLAB code described in Neville et al., 2025 (https://www.mdpi.com/2072-4292/17/12...,

浅季节性积雪在整个冬季的深度、层理特征与表面结构极少保持均一。空气动力学粗糙度长度（aerodynamic roughness length，z0）通常被视作水文与大气模型中的静态参数，但观测结果表明z0实为动态变量，因此需要对其开展精准的时空测量。

本研究于2019-2020冬季在科罗拉多州西北部的9处不同研究站点采集了地面激光雷达（terrestrial LiDAR）数据，以观测积雪表面的变化特征。通过112次野外站点观测获取的几何z0与雪深（snow depth，ds）数据表明，z0随ds发生变化：在积雪初始积累阶段，随着积雪贴合下伏地形，z0数值逐渐降低；当积雪厚度足够大（该阈值取决于地表粗糙度特征的高度）时，积雪表面会趋于均一；而当融雪过程启动后，积雪表面愈发不规则，z0数值随之升高。[原文后续内容存在截断]。
# 基于激光雷达的雪深表征积雪表面动态空气动力学粗糙度长度（z0）

数据集DOI：10.5061/dryad.80gb5mm35

## 数据与文件结构说明
本数据集包含9处野外站点在2019-2020冬季全时段的感兴趣区域（Area of Interest，AOI）点云数据。附带的Excel表格记录了各野外站点的观测信息，以及各点云数据对应的关联标签。

## 代码与软件
点云数据在Cloud Compare（开源项目，https://www.cloudcompare.org/）中构建与修改，但任何支持点云处理的软件均可适配本数据集。数据初始格式为.dat文件，若需在Cloud Compare中运行，需先将其转换为.txt格式。.dat格式文件可直接用于插值软件Surfer（Golden Software公司开发，https://www.goldensoftware.com/products/surfer/），随后可转换为数据集附带的.dat文件，以适配Neville等2025年发表的研究中提及的MATLAB代码（https://www.mdpi.com/2072-4292/17/12...）。